Sarcopenia, and its association with cardiometabolic and functional characteristics in Taiwan: Results from I-Lan Longitudinal Aging Study

Authors

  • Li-Kuo Liu,

    1. Aging and Health Research Center, National Yang Ming University, Taipei, Taiwan
    2. Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan
    Search for more papers by this author
  • Wei-Ju Lee,

    1. Aging and Health Research Center, National Yang Ming University, Taipei, Taiwan
    2. Department of Family Medicine, Taipei Veterans General Hospital Yuanshan Branch, Yi-Land, Taiwan
    Search for more papers by this author
  • Liang-Yu Chen,

    1. Aging and Health Research Center, National Yang Ming University, Taipei, Taiwan
    2. Institute of Public Health, National Yang Ming University, Taipei, Taiwan
    3. Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan
    Search for more papers by this author
  • An-Chun Hwang,

    1. Aging and Health Research Center, National Yang Ming University, Taipei, Taiwan
    2. Institute of Public Health, National Yang Ming University, Taipei, Taiwan
    3. Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan
    Search for more papers by this author
  • Ming-Hsien Lin,

    1. Aging and Health Research Center, National Yang Ming University, Taipei, Taiwan
    2. Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan
    Search for more papers by this author
  • Li-Ning Peng,

    1. Aging and Health Research Center, National Yang Ming University, Taipei, Taiwan
    2. Institute of Public Health, National Yang Ming University, Taipei, Taiwan
    3. Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan
    Search for more papers by this author
  • Liang-Kung Chen

    Corresponding author
    1. Aging and Health Research Center, National Yang Ming University, Taipei, Taiwan
    2. Institute of Public Health, National Yang Ming University, Taipei, Taiwan
    • Correspondence: Dr Liang-Kung Chen MD PhD, Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, No. 201, Sec 2, Shihpai Road, Taipei, Taiwan. Email: lkchen2@vghtpe.gov.tw

    Search for more papers by this author

Abstract

Aim

Sarcopenia is a well-recognized geriatric syndrome, which is associated with a variety of adverse outcomes. The present study aimed to evaluate the prevalence of sarcopenia and its associative clinical characteristics in Taiwan.

Methods

Data of the I-Lan Longitudinal Aging Study (ILAS) were retrieved for this study. Sarcopenia was defined according to the European Working Group on Sarcopenia in Older People criteria, and comparisons of demographic characteristics, physical performance, body composition, cardiometabolic profiles and functionality indicators were carried out.

Results

Overall, data of 1008 participants (mean age 65.2 ± 9.3 years, male 50.6%) were retrieved for analysis. The cut-off value of relative appendicular skeletal muscle was 7.0 kg/m2 for men and 5.9 kg/m2 for women. Sarcopenia was significantly related to low body mass index, smaller waist circumference, poor nutrition and poor cognition. The mean carotid intima-media thickness and cardiometabolic parameters showed no statistically significant findings.

Conclusions

The present paper showed the epidemiology of sarcopenia, and the strong connection to functionality indicators. However, sarcopenia was not associated with cardiometabolic risk or carotid intima media thickness in the present study. Geriatr Gerontol Int 2014; 14 (Suppl. 1): 36–45.

Introduction

In recent years, sarcopenia has been well recognized as a geriatric syndrome,[1] and has been defined as an age-related decline in skeletal muscle mass plus low muscle strength and/or low physical performance.[2, 3] The association of sarcopenia with various cardiometabolic disorders and diseases has been reported,[4, 5] such as diabetes,[6] chronic pulmonary diseases,[7] impaired heart functions[8, 9] and atherosclerosis.[10, 11] In addition, sarcopenia is also related to impaired functional status, frailty and poorer quality of life of older adults.[12] Sarcopenia is also associated with adverse clinical consequences, such as infectious and non-infectious complications of hospital inpatients, and all-cause mortality.[13, 14]

The European Working Group on Sarcopenia in Older People (EWGSOP) recommended a diagnostic algorithm and standardized measurements of sarcopenia,[3] which has been supported by a great variety of studies internationally. However, based on different assessment instruments and cut-off values in different study populations, the prevalence of sarcopenia varied from 3% to 30% from study to study.[15-17] As the measurements of muscle mass and muscle strength were closely related to body size and ethnic backgrounds, providing an optimal diagnosis for sarcopenia in Asian countries is challenging. In particular, the population demography forecast clearly showed the escalation of the elderly population in Asia, which might consequently increase the impact of sarcopenia in Asia. Although Taiwan is known to be one of the fastest aging countries in the world,[18, 19] little is known regarding the impact of sarcopenia in Taiwan.[20, 21] Therefore, the aim of the present study was to evaluate the prevalence of sarcopenia among the community-dwelling middle-aged and elderly population in Taiwan, and to provide comprehensive demographic and clinical characteristics, physical and mental function, health behaviors and nutritional status and cardiometabolic profiles for further studies of sarcopenia.

Methods

Study design

The I-Lan Longitudinal Aging Study (ILAS) is a population-based aging cohort study in I-Lan County of Taiwan. ILAS aimed to evaluate the complex interrelationship between aging, frailty, sarcopenia and cognitive decline. Community-dwelling adults aged 50 years and older were randomly sampled through the household registrations of the county government in Yuanshan Township of I-Lan County in Taiwan. Selected residents were invited to participate by mail or telephone invitations from the research team, and were enrolled when they had fully consented and agreed for participation. The inclusion criteria were: (i) inhabitants who then lived in I-Lan County without a plan to move in the near future; and (ii) inhabitants aged 50 years or older. Any respondents that met any one of the following conditions were excluded from the study: (i) the respondent was unable to communicate with the interviewer and grant an interview; (ii) the respondent had a poor function status, which could lead to a fail in evaluation, such as unable to complete a 6-m timed walk within a reasonable period of time; (iii) the respondent had a limited life expectancy (in general, <6 months) because of major illnesses; (iv) the respondent had an implant that was contraindicated for magnetic resonance imaging; and (v) currently institutionalized people. The whole study had been approved by the institutional review board of the National Yang Ming University.

Demographic and physical examinations

A questionnaire seeking information about demographics, smoking habit, habitual alcohol use status, educational years, medical history and comorbidities burden by Charlson Comorbidity Index[22] was administered by trained interviewers. Extensive functional measurements were collected through questionnaires by trained interviewers as well, including cognitive status carried out by the Mini-Mental State Examination (MMSE),[23] functional assessment by the Functional Autonomy Measurement System (SMAF; a tool of a 29-items scale ranging from 0 to 87 points, measured activities of daily living, instrumental activities of daily living, mental function, mobility and communications),[24] mood and depression status described by the Center for Epidemiologic Studies Depression Scale (CES-D),[25] and the nutritional status using Mini-Nutrition Assessment (MNA).[26]

All participants underwent anthropometric measurements by research nurses, including height, bodyweight and waist circumferences. Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. Systolic blood pressure and diastolic blood pressure were measured at rest and recorded.

Muscle strength and physical performance

Handgrip strength of the dominant hand was measured using digital dynamometers (Smedlay's Dynamo Meter; TTM, Tokyo, Japan) while the participant was in an upright standing position, with arms down by their sides. Participants needed to hold the dynamometer in the dominant hand without squeezing the arm against the body, and they were urged to exhibit the greatest possible force. The best result of three tests was used for statistical analysis.[27]

A timed 6-m walk was carried out to evaluate physical performance.[28, 29] Participants walked at their usual walking speed with a static start without deceleration throughout a 6-m straight line, while the time consumed was taken by a fixed study nurse with a stop watch (HS-70W, Casio computer co. LTC, Tokyo, Japan).

Body composition

A whole body dual-energy X-ray absorptiometry (DXA) scan was carried out for each participant to measure total body fat mass and percentage, and fat-free lean body mass (LBM) was measured using a Lunar Prodigy instrument (GE Healthcare, Madison, WI, USA). Appendicular skeletal muscle mass (ASM) was defined by the sum of the lean soft tissue mass of four limbs.[30] According to our previous study, height-adjusted muscle mass was a more suitable skeletal muscle index for Asian populations,[31] as proposed by Baumgartner et al.[32] Therefore, relative appendicular skeletal muscle (RASM), calculated by appendicular skeletal muscle mass divided by height (m) squared (ASM / height2; kg/m2) was used in the present study. Bone density was also obtained, and then lumbar and hip T-score were calculated.

Diagnosis of sarcopenia

In the present study, sarcopenia was defined by the EWGSOP criteria,[3] as low muscle mass plus low muscle strength (measured by handgrip strength) or low physical performance (measured by usual walking speed). To define the cut-off value of low muscle mass, the sex-specific lowest 20% of the study population was used, as proposed by Newman et al.[33] The cut-off values of handgrip strength and walking speed were managed in the same approach. Figure 1 presents the diagnostic algorithm for sarcopenia by the EWGSOP recommendations, and cut-off values of muscle mass, muscle strength and walking speed of the present study.

Figure 1.

Algorithm for sarcopenia definition and cut-off values using the I-Lan Longitudinal Aging Study database. The figure was modified from the European Working Group on Sarcopenia in Older People and Asian Working Group for Sarcopenia consensus. RASM, relative appendicular skeletal muscle index.

Carotid ultrasonography

In the present study, bilateral carotid arteries in longitudinal projections were investigated using an ultrasound instrument (GE LOGIQ 400 PRO; GE, Cleveland, OH, USA) equipped with a high-resolution broadband width linear array transducer. An experienced technician carried out the ultrasound examinations for all participants. The participants were examined in the supine position. Images were obtained bilaterally of the proximal common carotid artery (CCA) to distal CCA, including bifurcation, internal carotid artery and external carotid artery. Each participant had intima-media thickness (IMT) measured on the far wall of the CCA by longitudinal view.[34] Mean IMT, the average of the left and right IMT, was measured for further analysis.

Laboratory examinations

All blood samples were drawn with the participant in the seated position after a 10-h overnight fast. Serum concentrations of glucose, total cholesterol, low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) were determined using an automatic analyzer (ADVIA 1800, Siemens, Malvern, PA, USA). Whole-blood glycated hemoglobin A1c (HbA1c) was measured by an enzymatic method using the Tosoh G8 HPLC Analyzer (Tosoh Bioscience, Inc., San Francisco, CA, USA).

Serum levels of total testosterone were measured using a chemiluminescence immunoassay analyzer (Siemens ADVIA Centaur; Siemens). The intra-assay coefficient of variation (CV) and inter-assay CV were 5.8% and 4.7%, respectively. Dehydroepiandrosterone sulfate (DHEA-S) and sex-hormone binding globulin (SHBG) were measured by using electrochemiluminometry (Roche Elecsys e411; Roche, Indianapolis, MI, USA). Analytical sensitivity and intra- and inter-assay CV were as follows: DHEA-S: 0.1 µg/dL, 4.02%, 2.4%; and SHBG: 0.35 nmol/L; 2.57%, 2.7%, respectively. Free T and bio T were calculated from total testosterone, SHBG and albumin by the Vermeulen method.[35] Then the free androgen index (FAI) was defined as total testosterone (nM/L) / SHBG (nM/L). The serum level of growth hormone, homocysteine and insulin-like growth factor-1 (IGF-1) were also measured.

Statistical analysis

All descriptive continuous variables in the text and tables of the present study were reported as mean ± standard deviation, and categorical variables were expressed by number (percentage). Comparisons between continuous variables were carried out by Student's t-test, and comparisons of categorical data were carried out by χ2-test when indicated. The statistically significant factors in the single variable analysis were listed as independent variables, whereas sarcopenia was regarded as the dependent variable. Binary logistic regressions with backward Wald method were used to determine independent risk factors for GS. All statistical analysis was carried out by commercial statistical software (spss 16.0; SPSS, Chicago, IL, USA). A P-value of <0.05 (two-tailed) was considered statistically significant.

Results

In the present study, data of 1008 participants aged 50 years and older of ILAS were retrieved, and 25 of them were excluded as a result of incomplete data. Their mean age was 65.2 ± 9.3 years, and men accounted for 50.6% of participants. Among them, 497 were men (mean age 66.2 ± 9.8 years) and 486 were women (mean age 64.2 ± 8.7 years). Table 1 summarizes the demographic characteristics of the participants. The analysis showed that BMI was similar between men and women. However, women had higher total body fat percentage and higher total body fat mass than men. On the contrary, men had more lean body mass, skeletal muscle mass and higher skeletal muscle index (RASM). In physical performance, men walked faster (1.6 ± 0.5 m/s vs 1.4 ± 0.4 m/s, P < 0.001) and had significantly stronger handgrip strength than women (34.9 ± 8.3 kg vs 21.8 ± 5.3 kg, P < 0.001). In the present study, the multimorbidity of the study participants was low (CCI 0.9 ± 1.1 in men and 0.8 ± 1.2 in women) with highly preserved functional status, low score of depressive symptoms and well-nourished status. Nevertheless, the mean IMT of men were thicker than women.

Table 1. Demographic characteristics of participants of the I-Lan Longitudinal Aging Study
  Men (n = 497) Women (n = 486) P-value
  1. ASM, appendicular skeletal muscle mass; CES-D, the Center for Epidemiologic Studies Depression Scale; IMT, Intima-media thickness; MMSE, Mini-Mental State Examination; RASM, relative appendicular skeletal muscle index (appendicular skeletal muscle mass / height2); SMAF, the Functional Autonomy Measurement System.
Age (years)66.2 ± 9.864.2 ± 8.7<0.001
Anthropometric measurements   
Height (cm)163.7 ± 6.1152.8 ± 5.5<0.001
Weight (kg)66.9 ± 10.458.2 ± 9.9<0.001
Body mass index (kg/m2)24.9 ± 3.424.9 ± 3.90.859
Dual-energy X-ray absorptiometry 
Lean body mass (kg)48.1 ± 5.435.6 ± 4.2<0.001
ASM (kg)21.2 ± 2.914.8 ± 2.1<0.001
RASM (kg/m2)7.9 ± 0.86.3 ± 0.7<0.001
Total fat mass (kg)17.0 ± 6.421.6 ± 6.7<0.001
Total body fat percentage (%)25.4 ± 6.737.0 ± 6.4<0.001
Lumbar T-score−0.35 ± 1.50−1.28 ± 1.39<0.001
Hip T-score−0.77 ± 1.09−1.27 ± 1.10<0.001
Physical performance   
Walking speed (m/s)1.6 ± 0.51.4 ± 0.4<0.001
Handgrip strength (kg)34.9 ± 8.321.8 ± 5.3<0.001
Cigarette Smoking (%)   
Never35.194.5<0.001
Past27.20.8 
Present37.74.6 
Alcohol drinker (%)51.220.8<0.001
Functional status   
SMAF−0.3 ± 2.2−0.2 ± 1.80.398
CES-D2.6 ± 4.23.9 ± 6.5<0.001
Mini-nutrition assessment27.3 ± 1.826.8 ± 2.1<0.001
MMSE25.9 ± 3.724.7 ± 4.3<0.001
Education, years6.3 ± 4.94.4 ± 4.3<0.001
Charlson Comorbidity Index0.9 ± 1.10.8 ± 1.20.149
Mean carotid IMT (mm)0.7 ± 0.20.7 ± 0.1<0.001

The participants were divided into two groups for analysis. For those who were younger than 65 years, the comparison was carried out between the low muscle mass group (defined as the age- and sex-specific RASM mass within the lower 20% of this population) and normal muscle mass participants. For those aged 65 years and older, the diagnostic criteria of sarcopenia were applied, and the comparisons were made between participants with and without sarcopenia. Figure 1 shows the diagnosis of sarcopenia algorithm modified from the EWGSOP algorithm,[3] and 25 men (9.4%) and 21 women (9.8%) were categorized as having sarcopenia.

Table 2 summarized the comparisons between participants aged younger than 65 years with a normal amount or low muscle mass in both sexes. In both sexes, participants with low muscle mass were significantly thinner (both BMI and waist circumference) than those with normal muscle mass. However, the age was not statistically different between the two groups in both sexes. Furthermore, participants with low muscle mass had less lean body mass and less appendicular skeletal muscle mass when compared with their compatriots. The total body fat percentage showed no significant differences between groups in both sexes. Other associated factors are listed in Table 2.

Table 2. Comparisons between middle-aged participants with normal and low muscle mass by sex
 MenP-valueWomenP-value
NormalLow muscle massNormalLow muscle mass
  1. ASM, appendicular skeletal muscle mass; CES-D, the Center for Epidemiologic Studies Depression Scale; DBP, diastolic blood pressure; DHEA-S, dehydroepiandrosterone sulfate; HbA1c, glycated hemoglobin; IGF-1, insulin-like growth factor-1; IMT, intima-media thickness; MMSE, Mini-Mental State Examination; RASM, relative appendicular skeletal muscle index (appendicular skeletal muscle mass / height2); SBP, systolic blood pressure; SMAF, the Functional Autonomy Measurement System.
n18842 22349 
Age (years)57.2 ± 3.957.8 ± 4.10.38057.5 ± 3.757.7 ± 3.90.812
Body mass index (kg/m2)26.1 ± 3.422.8 ± 2.7<0.00125.4 ± 3.921.5 ± 2.1<0.001
Waist circumference (cm)88.8 ± 8.483.9 ± 7.60.00182.9 ± 12.875.7 ± 6.6<0.001
Dual-energy X-ray absorptiometry     
Lean body mass (kg)51.5 ± 4.945.3 ± 3.1<0.00136.6 ± 4.131.3 ± 2.6<0.001
ASM (kg)23.1 ± 2.519.5 ± 1.4<0.00115.5 ± 1.912.7 ± 1.3<0.001
RASM (ASM/ht2)8.4 ± 0.67.2 ± 0.3<0.0016.5 ± 0.75.3 ± 0.3<0.001
Total fat mass (kg)18.5 ± 6.915.0 ± 5.50.00122.6 ± 6.819.0 ± 4.6<0.001
Total body fat percentage (%)25.7 ± 6.924.2 ± 6.10.20537.5 ± 6.137.3 ± 6.10.821
Lumbar T-score−0.29 ± 1.21−0.47 ± 1.600.509−0.77 ± 1.32−1.13 ± 1.060.078
Hip T-score−0.40 ± 0.96−0.75 ± 1.130.041−0.83 ± 1.00−1.39 ± 0.94<0.001
Physical performance      
Walking speed (m/s)1.7 ± 00.51.7 ± 0.60.9501.5 ± 0.41.6 ± 0.40.001
Handgrip strength (kg)40.2 ± 6.637.4 ± 6.90.01823.9 ± 4.822.3 ± 4.9<0.001
Cigarette Smoking (%)      
Never43.735.70.34595.095.70.820
Past20.816.700
Present35.547.65.04.3
Alcohol drinker (%)67.064.30.73428.323.40.494
Functional status      
SMAF−0.40 ± 2.54−1.94 ± 5.560.193−0.17 ± 0.65−2.79 ± 8.130.156
CES-D2.7 ± 4.43.4 ± 4.70.4953.7 ± 6.24.2 ± 6.70.681
Mini-nutrition assessment27.1 ± 1.925.9 ± 2.40.00526.9 ± 1.824.9 ± 3.60.022
MMSE24.7 ± 3.721.1 ± 4.80.00122.5 ± 4.120.5 ± 5.40.030
Education (years)4.4 ± 4.51.6 ± 2.2<0.0011.6 ± 2.60.9 ± 1.80.209
Charlson Comorbidity Index1.2 ± 1.21.9 ± 1.30.0251.2 ± 1.31.7 ± 1.40.106
Mean carotid IMT (mm)0.8 ± 0.20.8 ± 0.10.4180.7 ± 0.10.7 ± 0.10.367
Cardiometabolic parameters      
SBP (mmHg)136.4 ± 17.7134.8 ± 14.00.667137.1 ± 19.9130.7 ± 12.90.150
DBP (mmHg)81.6 ± 12.680.5 ± 9.10.67582.3 ± 13.778.8 ± 12.40.265
Fasting blood glucose (mg/dL)100.1 ± 18.698.9 ± 35.60.792102.8 ± 24.2102.7 ± 19.60.992
HbA1c (%)6.0 ± 0.86.1 ± 1.20.8536.3 ± 0.96.3 ± 1.20.818
Total cholesterol (mg/dL)184.7 ± 33.3185.3 ± 34.40.927197.1 ± 35.2190.6 ± 36.60.407
LDL-cholesterol (mg/dL)115.1 ± 30.8117.1 ± 32.40.760118.6 ± 33.0110.7 ± 29.90.271
HDL-cholesterol (mg/dL)50.4 ± 12.246.4 ± 10.20.11856.5 ± 13.960.9 ± 13.90.152
Hormones and Endocrines      
Growth hormone (ng/mL)0.47 ± 0.781.12 ± 2.440.1970.63 ± 1.050.58 ± 0.640.818
Free Androgen Index (%)31.4 ± 10.227.6 ± 8.50.0782.1 ± 1.90.9 ± 0.6<0.001
DHEA-S (ug/dL)99.9 ± 60.482.7 ± 38.20.05166.5 ± 48.346.0 ± 33.40.050
Homocysteine (umol/L)16.5 ± 7.415.6 ± 5.80.55013.7 ± 5.015.2 ± 8.40.383
IGF-1 (ng/mL)120.4 ± 52.6115.0 ± 56.00.629111.6 ± 51.893.9 ± 40.00.116

Table 3 summarizes the comparisons between participants with and without sarcopenia in both sexes. In both sexes, participants with sarcopenia were significantly older and thinner (both BMI and waist circumference [in men]) than those without sarcopenia. Furthermore, sarcopenic participants also had less lean body mass and less appendicular skeletal muscle mass when compared with non-sarcopenic participants. The total body fat percentage showed no significant differences between groups in both sexes.

Table 3. Sex-specific comparisons of demographics and serum biomarkers in older adults with and without sarcopenia
 MenP-valueWomenP-value
NormalSarcopeniaNormalSarcopenia
  1. ASM, appendicular skeletal muscle mass; CES-D, the Center for Epidemiologic Studies Depression Scale; DBP, diastolic blood pressure; DHEA-S, dehydroepiandrosterone sulfate; HbA1c, glycated hemoglobin; IGF-1, insulin-like growth factor-1; IMT, intima-media thickness; MMSE, Mini-Mental State Examination; RASM, relative appendicular skeletal muscle index (appendicular skeletal muscle mass / height2); SBP, systolic blood pressure; SMAF, the Functional Autonomy Measurement System.
n24225 19321 
Age (years)73.5 ± 6.178.5 ± 4.1<0.00172.2 ± 4.976.4 ± 5.2<0.001
Body mass index (kg/m2)24.7 ± 3.121.7 ± 2.7<0.00125.4 ± 3.622.6 ± 4.20.001
Waist circumference (cm)88.9 ± 9.083.5 ± 11.40.00686.0 ± 10.383.4 ± 11.10.277
Dual-energy X-ray absorptiometry     
Lean body mass (kg)47.0 ± 4.741.1 ± 3.4<0.00136.0 ± 3.831.7 ± 3.1<0.001
ASM (kg)20.5 ± 2.417.1 ± 1.6<0.00114.8 ± 1.912.5 ± 1.3<0.001
RASM (ASM/ht2)7.7 ± 0.76.6 ± 0.5<0.0016.5 ± 0.65.5 ± 0.4<0.001
Total fat mass (kg)16.7 ± 5.913.4 ± 6.40.00921.3 ± 6.419.2 ± 8.70.165
Total body fat percentage (%)25.6 ± 6.623.6 ± 7.30.14036.5 ± 6.335.9 ± 9.90.781
Lumbar T-score−0.33 ± 1.65−0.62 ± 1.600.412−1.78 ± 1.30−2.36 ± 1.420.064
Hip T-score−0.95 ± 1.04−1.63 ± 1.410.004−1.61 ± 1.01−2.28 ± 1.370.007
Physical performance      
Walking speed (m/s)1.5 ± 0.51.1 ± 0.3<0.0011.2 ± 0.40.9 ± 0.20.001
Handgrip strength (kg)31.7 ± 6.923.0 ± 5.5<0.00120.3 ± 4.513.5 ± 4.3<0.001
Cigarette smoking (%)      
Never29.520.80.56994.785.7<0.001
Past32.541.70.514.3
Present38.037.54.80
Alcohol drinker (%)37.641.70.69612.214.30.780
Functional status      
SMAF−0.40 ± 2.54−1.94 ± 5.560.193−0.17 ± 0.65−2.79 ± 8.130.156
CES-D2.7 ± 4.43.4 ± 4.70.4953.6 ± 6.24.6 ± 6.90.484
Mini-Nutrition Assessment27.1 ± 1.925.9 ± 2.40.00526.9 ± 1.824.9 ± 3.60.022
MMSE24.7 ± 3.721.1 ± 4.80.00122.6 ± 4.120.2 ± 5.40.015
Education (years)4.4 ± 4.51.6 ± 2.2<0.0011.6 ± 2.60.9 ± 1.90.126
Charlson Comorbidity Index1.2 ± 1.21.9 ± 1.30.0251.2 ± 1.31.9 ± 1.40.030
Mean carotid IMT (mm)0.8 ± 0.20.8 ± 0.10.4180.7 ± 0.10.7 ± 0.10.283
Cardiometabolic parameters      
SBP (mmHg)136.4 ± 17.7134.8 ± 14.00.667137.1 ± 19.9130.7 ± 12.90.150
DBP (mmHg)81.6 ± 12.680.5 ± 9.10.67582.3 ± 13.778.8 ± 12.40.265
Fasting blood glucose (mg/dL)100.1 ± 18.698.9 ± 35.60.792102.8 ± 24.1102.9 ± 20.60.980
HbA1c (%)6.0 ± 0.86.1 ± 1.20.8536.3 ± 0.96.3 ± 1.20.677
Total cholesterol (mg/dL)184.7 ± 33.3185.3 ± 34.40.927197.3 ± 35.2187.7 ± 35.40.234
LDL-cholesterol (mg/dL)115.1 ± 30.8117.1 ± 32.40.760118.7 ± 33.0109.1 ± 29.40.202
HDL-cholesterol (mg/dL)50.4 ± 12.246.4 ± 10.20.11856.7 ± 13.959.9 ± 14.10.320
Hormones and endocrines      
Growth hormone (ng/mL)0.47 ± 0.781.12 ± 2.440.1970.63 ± 1.050.58 ± 0.640.818
Free Androgen Index (%)31.4 ± 10.227.6 ± 8.50.0782.1 ± 1.90.9 ± 0.6<0.001
DHEA-S (µg/dL)99.9 ± 60.482.7 ± 38.20.05166.5 ± 48.346.0 ± 33.40.050
Homocysteine (umol/L)16.5 ± 7.415.6 ± 5.80.55013.7 ± 5.015.2 ± 8.40.383
IGF-1 (ng/mL)120.4 ± 52.6115.0 ± 56.00.629111.6 ± 51.893.9 ± 40.00.116

Participants with sarcopenia had a higher cigarette smoking rate in women, but no differences in habitual alcohol consumption in both sexes. The sarcopenia group, compared with those without sarcopenia, had a higher Charlson Comorbidity Index, poorer cognitive performance based on MMSE score, fewer educational years and poorer nutritional status by Mini-Nutrition Assessment. The participants with sarcopenia also showed a trend of poorer functional state by SMAF and more depressive symptoms by CES-D, though they were not statistically different. In terms of mean carotid IMT and cardiometabolic parameters, no significant differences could be identified between groups with or without sarcopenia in both sexes.

Considering hormonal change, there were no statistical differences in growth hormone, homocysteine or IGF-1. The serum free androgen index was lower in the sarcopenic group compared with the other group in both sexes, and the differences in dehydroepiandrosterone sulfate (DHEA-S) were borderline.

The statistically significant factors in the single variable analysis, including age, BMI, cigarette smoking, SMAF, MNA, MMSE, CCI, serum growth hormone and DHEA-S level were listed as independent univariate variables, whereas sarcopenia was regarded as the dependent variable. By using binary logistic regression (backward Wald method), we found that older age (odds ratio [OR] 1.112, 95% confidence interval [CI] 1.041–1.188; P = 0.002) and a lower BMI (OR 0.774, 95% CI 0.679–0.883; P < 0.001) were both independent risk factors for the presence of sarcopenia. (Table 4)

Table 4. Binary logistic regression on sarcopenia in I-Lan Longitudinal Aging Study older population
DependentIndependentOR (95% CI)
UnivariateMultivariate*
  1. *Backward Wald method was used in multivariate binary logistic regression with all variables entered at the beginning. Odds ratios and confidence intervals were presented only for significant variables with P < 0.05. BMI, body mass index; CCI, Charlson Comorbidity Index; CI, confidence interval; DHEA-S, dehydroepiandrosterone sulfate; MMSE, Mini-Mental State Examination; MNA, Mini-Nutrition Assessment; OR, odds ratio; SMAF, the Functional Autonomy Measurement System.
SarcopeniaAge1.135 (1.079–1.194)1.112 (1.041–1.188)
 Sex  

 Women

 Men

Reference

0.858 (0.472–1.559)

 
 BMI0.748 (0.670–0.835)0.774 (0.679–0.883)
 Cigarette smoking  
 NeverReference 
 Past0.545 (0.222–1.342) 
 Present0.554 (0.268–1.146) 
 SMAF0.881 (0.809–0.961) 
MNA0.747 (0.657–0.850) 
MMSE0.863 (0.808–0.922) 
 CCI1.382 (1.088–1.757) 
 Growth hormone1.221 (0.992–1.053) 
 DHEA-S0.992 (0.986–0.999) 

Discussion

The present study showed the prevalence of sarcopenia among community-dwelling middle-aged and elderly people in Taiwan. The prevalence of sarcopenia among the ILAS men and women (mean age 65 years) was 9.4% and 9.8%, respectively. Female sex was regarded as a risk factor of sarcopenia and a higher prevalence has been reported before,[1, 36] but many Asian and other international studies showed the prevalence of sarcopenia to be lower in women than in men.[37, 38] The cut-off values of relative skeletal muscle index in the present study (men: 7.0 kg/m2, women: 5.9 kg/m2) were similar to the Rosetta study,[32] Health ABC study[39] and some Asian reports.[40] However, unlike our previous study, the cut-off of skeletal muscle mass index was not determined by the sex-specific young reference group.[21] A similar approach has been reported by Newman, et al., which might be more suitable for Asian populations, because many Asian sarcopenia researchers encountered the same challenge that the prevalence of sarcopenia was extremely low in elderly women.[21, 40] To overcome this difficulty, some researchers prefer using weight-adjusted skeletal muscle mass instead of height-adjusted skeletal muscle mass. However, it has been clearly shown that the “low muscle mass” defined by height-adjusted and weight-adjusted skeletal muscle index eventually differ from each other greatly.[41] To be more compatible with functional status, we prefer using height-adjustment rather than weight-adjustment. However, using the 20th percentile of height-adjusted skeletal muscle index of the study sample as the cut-off value of low muscle mass might be a better approach for sarcopenia.

The prevalence of sarcopenia among men and women aged 65 years and older in ILAS was 9.4% and 9.8%, and would increase to 18.6% and 15.0% in men and women aged 80 years and older. The increasing prevalence along with aging is similar to that in the iISIRENTE study,[15] and many others. In the present study, sarcopenic participants were significantly thinner than non-sarcopenic participants of both sexes. Sarcopenic participants also had lower lean body mass and lower appendicular skeletal muscle mass compared with non-sarcopenic participants. Domiciano et al. also showed that more than 95% of sarcopenic participants defined by RASM were lean.[42] In theory, sarcopenia is more prone to co-occur among frail older people with low BMI, and is more likely to be associated with adverse consequences. In terms of obesity in the elderly, it is clear that mortality and morbidity only increase when a BMI is higher than 30 kg/m2,[43] which is far more obese than the present study population. Further outcome-based cohort research is required to clarify the clinical impact of sarcopenia defined by height-adjusted or weight-adjusted skeletal muscle index.

In the present study, sarcopenic participants showed poorer nutritional status and poorer cognitive function with fewer educational years compared with those without sarcopenia. However, the physical function and depressive symptoms were not significantly different between participants with and without sarcopenia, which was consistent with many previous studies.[44-46] The higher multimorbidities in sarcopenic participants in the present study was compatible with many studies, because the prevalence of metabolic syndrome and cardiovascular disease were more common in sarcopenic participants.[47, 48] However, the cardiometabolic profile of sarcopenic participants was similar to non-sarcopenic participants in the present study. Besides, a close relationship between sarcopenia and bone mass has been reported,[49, 50] which was compatible with the present study, especially in women.

In the present study, the mean carotid IMT and all cardiometabolic parameters were similar between participants with and without sarcopenia. Peters, et al. showed that the carotid intima-media was more echolucent in older adults than in middle-aged people,[51] which could underestimate the IMT of the study participants. A strong relationship between reduced thigh muscle mass area and carotid IMT has been reported,[11, 52] and physical activity was favorably associated with IMT in adolescents.[53] In theory, based on the aforementioned discoveries, sarcopenia should be positively associated with carotid IMT in the elderly, but this association was not identified in the present study. Similar to the present results, Abe, et al. also showed that the cardiovascular risk parameters were similar among participants with or without sarcopenia.[54] However, some studies clearly showed the association between metabolic syndrome and sarcopenic obesity or frailty,[48, 55, 56] which deserve further investigation in the future.

Despite the extensive effort that went into the present study, there were still some limitations. First, the study had a cross-sectional study design, so it was not possible to obtain the causal relationship. However, as ILAS is a longitudinal cohort study per se, we believe more outcome-based study results will be available in the near future. Second, the determination of cut-offs for low muscle mass, low muscle strength and low physical performance was based on the study sample. The ILAS participants were relatively healthier than community-dwelling older adults because we basically excluded participants with any disability. Therefore, the study results might underestimate the prevalence of sarcopenia and its associated health impact. However, the recruitment strategy was of great help in providing the functional trajectory of healthy older people living in communities. Third, study participants were recruited from a suburban area, and they were leading a more physically active lifestyle, which might be different from subjects enrolled from urban areas.

In conclusion, the prevalence of sarcopenia in the present study was similar to most previous Asian reports, and was lower than that from Western countries. Sarcopenic participants were slightly older, remarkably thinner, lower in educational levels, more likely to smoke (in women), lower in bone mineral density, with poorer cognitive function and poorer nutritional status. However, sarcopenia was not associated with cardiometabolic risk and carotid IMT. Further study is required to evaluate the clinical impact of sarcopenia, and the interrelationship with physical, mental function and mood status.

Disclosure statement

The authors declare no conflict of interest.

Ancillary